Friday, January 20, 2012

Machine-Learning with Real-time & Streaming Applications

We are organizing a meeting on the UC Berkeley campus on a topic which may be of interest to your Nuit-Blanche website and may warrant being mentioned under your "CS meetings related to compressive sensing". If interested, please feel free to add this conference or disseminate the following announcement.

We are experiencing a revolution in the capacity to quickly collect and transport large amounts of data. Not only has this revolution changed the means by which we store and access this data, but has also caused a fundamental transformation in the methods and algorithms that we use to extract knowledge from data. In scientific fields as diverse as climatology, medical science, astrophysics, particle physics, computer vision, and computational finance, massive streaming data sets have sparked innovation in methodologies for knowledge discovery in data streams. Cutting-edge methodology for streaming data has come from a number of diverse directions, from on-line learning, randomized linear algebra and approximate methods, to distributed optimization

methodology for cloud computing, to multi-class classification problems in the presence of noisy and spurious data.

This conference will bring together researchers from applied mathematics and several diverse scientific fields to discuss the current state of the art and open research questions in streaming data and real-time machine learning. The conference will be domain driven, with talks focusing on well-defined areas of application and describing the techniques and algorithms necessary to address the current and future challenges in the field.

Sessions will be accessible to a broad audience and will have a single track format with additional rooms for breakout sessions and posters. There will be no formal conference proceedings, but conference applicants are encouraged to submit an abstract and present a talk and/or poster.